from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-31 14:14:32.279614
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Thu, 31, Dec, 2020
Time: 14:14:35
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.3512
Nobs: 157.000 HQIC: -45.3916
Log likelihood: 1704.14 FPE: 9.51321e-21
AIC: -46.1032 Det(Omega_mle): 5.45727e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.460566 0.158938 2.898 0.004
L1.Burgenland 0.139372 0.081046 1.720 0.085
L1.Kärnten -0.235053 0.065164 -3.607 0.000
L1.Niederösterreich 0.114010 0.188792 0.604 0.546
L1.Oberösterreich 0.254969 0.161419 1.580 0.114
L1.Salzburg 0.172115 0.083666 2.057 0.040
L1.Steiermark 0.081877 0.115880 0.707 0.480
L1.Tirol 0.147905 0.077550 1.907 0.056
L1.Vorarlberg 0.003951 0.074184 0.053 0.958
L1.Wien -0.122977 0.156087 -0.788 0.431
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.514819 0.205754 2.502 0.012
L1.Burgenland 0.012199 0.104919 0.116 0.907
L1.Kärnten 0.367044 0.084359 4.351 0.000
L1.Niederösterreich 0.133586 0.244402 0.547 0.585
L1.Oberösterreich -0.187103 0.208966 -0.895 0.371
L1.Salzburg 0.187651 0.108310 1.733 0.083
L1.Steiermark 0.252000 0.150014 1.680 0.093
L1.Tirol 0.142250 0.100393 1.417 0.157
L1.Vorarlberg 0.176199 0.096035 1.835 0.067
L1.Wien -0.582916 0.202064 -2.885 0.004
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.293822 0.069187 4.247 0.000
L1.Burgenland 0.106199 0.035280 3.010 0.003
L1.Kärnten -0.024775 0.028367 -0.873 0.382
L1.Niederösterreich 0.072465 0.082183 0.882 0.378
L1.Oberösterreich 0.292660 0.070268 4.165 0.000
L1.Salzburg -0.005632 0.036421 -0.155 0.877
L1.Steiermark -0.020425 0.050444 -0.405 0.686
L1.Tirol 0.088715 0.033759 2.628 0.009
L1.Vorarlberg 0.125595 0.032293 3.889 0.000
L1.Wien 0.077577 0.067947 1.142 0.254
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.204517 0.080307 2.547 0.011
L1.Burgenland -0.012335 0.040950 -0.301 0.763
L1.Kärnten 0.022252 0.032926 0.676 0.499
L1.Niederösterreich 0.026301 0.095392 0.276 0.783
L1.Oberösterreich 0.412268 0.081561 5.055 0.000
L1.Salzburg 0.097243 0.042274 2.300 0.021
L1.Steiermark 0.182141 0.058551 3.111 0.002
L1.Tirol 0.033116 0.039184 0.845 0.398
L1.Vorarlberg 0.095242 0.037483 2.541 0.011
L1.Wien -0.062113 0.078867 -0.788 0.431
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.590842 0.166767 3.543 0.000
L1.Burgenland 0.066511 0.085038 0.782 0.434
L1.Kärnten 0.003840 0.068374 0.056 0.955
L1.Niederösterreich -0.047771 0.198092 -0.241 0.809
L1.Oberösterreich 0.159237 0.169370 0.940 0.347
L1.Salzburg 0.053807 0.087787 0.613 0.540
L1.Steiermark 0.113604 0.121588 0.934 0.350
L1.Tirol 0.213974 0.081370 2.630 0.009
L1.Vorarlberg 0.007868 0.077838 0.101 0.919
L1.Wien -0.144671 0.163776 -0.883 0.377
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.158302 0.116624 1.357 0.175
L1.Burgenland -0.027641 0.059469 -0.465 0.642
L1.Kärnten -0.012200 0.047816 -0.255 0.799
L1.Niederösterreich 0.174926 0.138531 1.263 0.207
L1.Oberösterreich 0.398199 0.118445 3.362 0.001
L1.Salzburg -0.029808 0.061392 -0.486 0.627
L1.Steiermark -0.045837 0.085030 -0.539 0.590
L1.Tirol 0.189374 0.056904 3.328 0.001
L1.Vorarlberg 0.038428 0.054434 0.706 0.480
L1.Wien 0.164099 0.114533 1.433 0.152
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.236218 0.145919 1.619 0.105
L1.Burgenland 0.062183 0.074407 0.836 0.403
L1.Kärnten -0.044878 0.059826 -0.750 0.453
L1.Niederösterreich -0.031186 0.173328 -0.180 0.857
L1.Oberösterreich -0.100285 0.148197 -0.677 0.499
L1.Salzburg 0.006180 0.076813 0.080 0.936
L1.Steiermark 0.381471 0.106388 3.586 0.000
L1.Tirol 0.521096 0.071198 7.319 0.000
L1.Vorarlberg 0.197447 0.068107 2.899 0.004
L1.Wien -0.225393 0.143302 -1.573 0.116
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.121281 0.171251 0.708 0.479
L1.Burgenland 0.013578 0.087325 0.155 0.876
L1.Kärnten -0.114464 0.070213 -1.630 0.103
L1.Niederösterreich 0.218105 0.203418 1.072 0.284
L1.Oberösterreich 0.010544 0.173924 0.061 0.952
L1.Salzburg 0.221779 0.090148 2.460 0.014
L1.Steiermark 0.142209 0.124858 1.139 0.255
L1.Tirol 0.094466 0.083558 1.131 0.258
L1.Vorarlberg 0.015886 0.079931 0.199 0.842
L1.Wien 0.287716 0.168179 1.711 0.087
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.585083 0.093933 6.229 0.000
L1.Burgenland -0.021242 0.047899 -0.443 0.657
L1.Kärnten 0.001019 0.038512 0.026 0.979
L1.Niederösterreich -0.009570 0.111577 -0.086 0.932
L1.Oberösterreich 0.280104 0.095399 2.936 0.003
L1.Salzburg 0.010157 0.049447 0.205 0.837
L1.Steiermark 0.000820 0.068486 0.012 0.990
L1.Tirol 0.078383 0.045832 1.710 0.087
L1.Vorarlberg 0.172031 0.043843 3.924 0.000
L1.Wien -0.092094 0.092248 -0.998 0.318
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.140339 -0.003861 0.206693 0.247383 0.059187 0.101210 -0.080699 0.164797
Kärnten 0.140339 1.000000 -0.004246 0.186829 0.132405 -0.142801 0.174801 0.031979 0.298173
Niederösterreich -0.003861 -0.004246 1.000000 0.265800 0.082652 0.203868 0.104834 0.045503 0.353458
Oberösterreich 0.206693 0.186829 0.265800 1.000000 0.278314 0.294384 0.111341 0.076583 0.108071
Salzburg 0.247383 0.132405 0.082652 0.278314 1.000000 0.147062 0.069962 0.081369 -0.025451
Steiermark 0.059187 -0.142801 0.203868 0.294384 0.147062 1.000000 0.107539 0.089937 -0.129203
Tirol 0.101210 0.174801 0.104834 0.111341 0.069962 0.107539 1.000000 0.149999 0.133540
Vorarlberg -0.080699 0.031979 0.045503 0.076583 0.081369 0.089937 0.149999 1.000000 0.099258
Wien 0.164797 0.298173 0.353458 0.108071 -0.025451 -0.129203 0.133540 0.099258 1.000000